id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.05729 | ExPO: Explainable Phonetic Trait-Oriented Network for Speaker
Verification | [
"cs.SD",
"cs.AI",
"eess.AS"
] | In speaker verification, we use computational method to verify if an utterance matches the identity of an enrolled speaker. This task is similar to the manual task of forensic voice comparison, where linguistic analysis is combined with auditory measurements to compare and evaluate voice samples. Despite much success, ... | {
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2501.05730 | Element-wise Attention Is All You Need | [
"cs.LG",
"cs.AI"
] | The self-attention (SA) mechanism has demonstrated superior performance across various domains, yet it suffers from substantial complexity during both training and inference. The next-generation architecture, aiming at retaining the competitive performance of SA while achieving low-cost inference and efficient long-seq... | {
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2501.05731 | Diving Deep: Forecasting Sea Surface Temperatures and Anomalies | [
"cs.LG",
"physics.ao-ph",
"stat.AP"
] | This overview paper details the findings from the Diving Deep: Forecasting Sea Surface Temperatures and Anomalies Challenge at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) 2024. The challenge focused on the data-driven predictability of global s... | {
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2501.05733 | TB-Bench: Training and Testing Multi-Modal AI for Understanding
Spatio-Temporal Traffic Behaviors from Dashcam Images/Videos | [
"cs.CV"
] | The application of Multi-modal Large Language Models (MLLMs) in Autonomous Driving (AD) faces significant challenges due to their limited training on traffic-specific data and the absence of dedicated benchmarks for spatiotemporal understanding. This study addresses these issues by proposing TB-Bench, a comprehensive b... | {
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2501.05735 | ELENA: Epigenetic Learning through Evolved Neural Adaptation | [
"cs.NE",
"cs.LG"
] | Despite the success of metaheuristic algorithms in solving complex network optimization problems, they often struggle with adaptation, especially in dynamic or high-dimensional search spaces. Traditional approaches can become stuck in local optima, leading to inefficient exploration and suboptimal solutions. Most of th... | {
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2501.05744 | LLVD: LSTM-based Explicit Motion Modeling in Latent Space for Blind
Video Denoising | [
"cs.CV",
"cs.LG"
] | Video restoration plays a pivotal role in revitalizing degraded video content by rectifying imperfections caused by various degradations introduced during capturing (sensor noise, motion blur, etc.), saving/sharing (compression, resizing, etc.) and editing. This paper introduces a novel algorithm designed for scenarios... | {
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2501.05745 | Covariate Dependent Mixture of Bayesian Networks | [
"stat.ML",
"cs.LG"
] | Learning the structure of Bayesian networks from data provides insights into underlying processes and the causal relationships that generate the data, but its usefulness depends on the homogeneity of the data population, a condition often violated in real-world applications. In such cases, using a single network struct... | {
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2501.05748 | From Bit to Block: Decoding on Erasure Channels | [
"cs.IT",
"math.IT"
] | We provide a general framework for bounding the block error threshold of a linear code $C\subseteq \mathbb{F}_2^N$ over the erasure channel in terms of its bit error threshold. Our approach relies on understanding the minimum support weight of any $r$-dimensional subcode of $C$, for all small values of $r$. As a proof ... | {
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2501.05749 | Bridging Dialects: Translating Standard Bangla to Regional Variants
Using Neural Models | [
"cs.CL"
] | The Bangla language includes many regional dialects, adding to its cultural richness. The translation of Bangla Language into regional dialects presents a challenge due to significant variations in vocabulary, pronunciation, and sentence structure across regions like Chittagong, Sylhet, Barishal, Noakhali, and Mymensin... | {
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2501.05750 | Semantic Mapping in Indoor Embodied AI -- A Comprehensive Survey and
Future Directions | [
"cs.RO",
"cs.CV"
] | Intelligent embodied agents (e.g. robots) need to perform complex semantic tasks in unfamiliar environments. Among many skills that the agents need to possess, building and maintaining a semantic map of the environment is most crucial in long-horizon tasks. A semantic map captures information about the environment in a... | {
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2501.05752 | Semantic Exploration with Adaptive Gating for Efficient Problem Solving
with Language Models | [
"cs.AI",
"cs.CL"
] | Recent advancements in large language models (LLMs) have shown remarkable potential in various complex tasks requiring multi-step reasoning methods like tree search to explore diverse reasoning paths. However, existing methods often suffer from computational inefficiency and redundancy. First, they overlook the diversi... | {
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2501.05755 | CognoSpeak: an automatic, remote assessment of early cognitive decline
in real-world conversational speech | [
"cs.SD",
"cs.LG",
"eess.AS"
] | The early signs of cognitive decline are often noticeable in conversational speech, and identifying those signs is crucial in dealing with later and more serious stages of neurodegenerative diseases. Clinical detection is costly and time-consuming and although there has been recent progress in the automatic detection o... | {
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2501.05757 | Locality-aware Gaussian Compression for Fast and High-quality Rendering | [
"cs.CV"
] | We present LocoGS, a locality-aware 3D Gaussian Splatting (3DGS) framework that exploits the spatial coherence of 3D Gaussians for compact modeling of volumetric scenes. To this end, we first analyze the local coherence of 3D Gaussian attributes, and propose a novel locality-aware 3D Gaussian representation that effect... | {
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2501.05762 | Development and Comparison of Model-Based and Data-Driven Approaches for
the Prediction of the Mechanical Properties of Lattice Structures | [
"cond-mat.soft",
"cs.CE",
"cs.LG",
"physics.comp-ph"
] | Lattice structures have great potential for several application fields ranging from medical and tissue engineering to aeronautical one. Their development is further speeded up by the continuing advances in additive manufacturing technologies that allow to overcome issues typical of standard processes and to propose tai... | {
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2501.05763 | StarGen: A Spatiotemporal Autoregression Framework with Video Diffusion
Model for Scalable and Controllable Scene Generation | [
"cs.CV"
] | Recent advances in large reconstruction and generative models have significantly improved scene reconstruction and novel view generation. However, due to compute limitations, each inference with these large models is confined to a small area, making long-range consistent scene generation challenging. To address this, w... | {
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2501.05764 | Controlling Large Language Models Through Concept Activation Vectors | [
"cs.CL"
] | As large language models (LLMs) are widely deployed across various domains, the ability to control their generated outputs has become more critical. This control involves aligning LLMs outputs with human values and ethical principles or customizing LLMs on specific topics or styles for individual users. Existing contro... | {
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2501.05765 | Deontic Temporal Logic for Formal Verification of AI Ethics | [
"cs.AI",
"cs.LO"
] | Ensuring ethical behavior in Artificial Intelligence (AI) systems amidst their increasing ubiquity and influence is a major concern the world over. The use of formal methods in AI ethics is a possible crucial approach for specifying and verifying the ethical behavior of AI systems. This paper proposes a formalization b... | {
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2501.05767 | Migician: Revealing the Magic of Free-Form Multi-Image Grounding in
Multimodal Large Language Models | [
"cs.CL",
"cs.AI",
"cs.CV"
] | The recent advancement of Multimodal Large Language Models (MLLMs) has significantly improved their fine-grained perception of single images and general comprehension across multiple images. However, existing MLLMs still face challenges in achieving precise grounding in complex multi-image scenarios. To address this, w... | {
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2501.05768 | Halal or Not: Knowledge Graph Completion for Predicting Cultural
Appropriateness of Daily Products | [
"cs.LG",
"cs.AI"
] | The growing demand for halal cosmetic products has exposed significant challenges, especially in Muslim-majority countries. Recently, various machine learning-based strategies, e.g., image-based methods, have shown remarkable success in predicting the halal status of cosmetics. However, these methods mainly focus on an... | {
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2501.05769 | Conditional Diffusion Model for Electrical Impedance Tomography | [
"cs.CV"
] | Electrical impedance tomography (EIT) is a non-invasive imaging technique, which has been widely used in the fields of industrial inspection, medical monitoring and tactile sensing. However, due to the inherent non-linearity and ill-conditioned nature of the EIT inverse problem, the reconstructed image is highly sensit... | {
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2501.05770 | Path Planning for Multi-Copter UAV Formation Employing a Generalized
Particle Swarm Optimization | [
"cs.RO",
"cs.SY",
"eess.SY"
] | The paper investigates the problem of path planning techniques for multi-copter uncrewed aerial vehicles (UAV) cooperation in a formation shape to examine surrounding surfaces. We first describe the problem as a joint objective cost for planning a path of the formation centroid working in a complicated space. The path ... | {
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2501.05772 | rmlnomogram: An R package to construct an explainable nomogram for any
machine learning algorithms | [
"cs.LG",
"stat.ML"
] | Background: Current nomogram can only be created for regression algorithm. Providing nomogram for any machine learning (ML) algorithms may accelerate model deployment in clinical settings or improve model availability. We developed an R package and web application to construct nomogram with model explainability of any ... | {
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2501.05775 | STHFL: Spatio-Temporal Heterogeneous Federated Learning | [
"cs.LG",
"cs.DC"
] | Federated learning is a new framework that protects data privacy and allows multiple devices to cooperate in training machine learning models. Previous studies have proposed multiple approaches to eliminate the challenges posed by non-iid data and inter-domain heterogeneity issues. However, they ignore the \textbf{spat... | {
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2501.05777 | StructSR: Refuse Spurious Details in Real-World Image Super-Resolution | [
"cs.CV"
] | Diffusion-based models have shown great promise in real-world image super-resolution (Real-ISR), but often generate content with structural errors and spurious texture details due to the empirical priors and illusions of these models. To address this issue, we introduce StructSR, a simple, effective, and plug-and-play ... | {
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2501.05778 | Formally Verified Neural Lyapunov Function for Incremental
Input-to-State Stability of Unknown Systems | [
"eess.SY",
"cs.SY"
] | This work presents an approach to synthesize a Lyapunov-like function to ensure incrementally input-to-state stability ($\delta$-ISS) property for an unknown discrete-time system. To deal with challenges posed by unknown system dynamics, we parameterize the Lyapunov-like function as a neural network, which we train usi... | {
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2501.05780 | Multi-layer RIS on Edge: Communication, Computation and Wireless Power
Transfer | [
"cs.IT",
"math.IT"
] | The rapid expansion of Internet of Things (IoT) and its integration into various applications highlight the need for advanced communication, computation, and energy transfer techniques. However, the traditional hardware-based evolution of communication systems faces challenges due to excessive power consumption and pro... | {
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2501.05783 | UV-Attack: Physical-World Adversarial Attacks for Person Detection via
Dynamic-NeRF-based UV Mapping | [
"cs.CV",
"cs.AI"
] | In recent research, adversarial attacks on person detectors using patches or static 3D model-based texture modifications have struggled with low success rates due to the flexible nature of human movement. Modeling the 3D deformations caused by various actions has been a major challenge. Fortunately, advancements in Neu... | {
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2501.05786 | Cryptanalysis of Cancelable Biometrics Vault | [
"cs.CR",
"cs.CV"
] | Cancelable Biometrics (CB) stands for a range of biometric transformation schemes combining biometrics with user specific tokens to generate secure templates. Required properties are the irreversibility, unlikability and recognition accuracy of templates while making their revocation possible. In biometrics, a key-bind... | {
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2501.05787 | MARS6: A Small and Robust Hierarchical-Codec Text-to-Speech Model | [
"eess.AS",
"cs.CL"
] | Codec-based text-to-speech (TTS) models have shown impressive quality with zero-shot voice cloning abilities. However, they often struggle with more expressive references or complex text inputs. We present MARS6, a robust encoder-decoder transformer for rapid, expressive TTS. MARS6 is built on recent improvements in sp... | {
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2501.05790 | Understanding Impact of Human Feedback via Influence Functions | [
"cs.AI",
"cs.HC",
"cs.LG"
] | In Reinforcement Learning from Human Feedback (RLHF), it is crucial to learn suitable reward models from human feedback to align large language models (LLMs) with human intentions. However, human feedback can often be noisy, inconsistent, or biased, especially when evaluating complex responses. Such feedback can lead t... | {
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2501.05795 | Robust Counterfactual Explanations under Model Multiplicity Using
Multi-Objective Optimization | [
"cs.LG",
"cs.AI"
] | In recent years, explainability in machine learning has gained importance. In this context, counterfactual explanation (CE), which is an explanation method that uses examples, has attracted attention. However, it has been pointed out that CE is not robust when there are multiple machine-learning models with similar acc... | {
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2501.05803 | Test-time Alignment of Diffusion Models without Reward Over-optimization | [
"cs.LG",
"cs.AI",
"cs.CV",
"math.ST",
"stat.TH"
] | Diffusion models excel in generative tasks, but aligning them with specific objectives while maintaining their versatility remains challenging. Existing fine-tuning methods often suffer from reward over-optimization, while approximate guidance approaches fail to optimize target rewards effectively. Addressing these lim... | {
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2501.05808 | Real-Time Integrated Dispatching and Idle Fleet Steering with Deep
Reinforcement Learning for A Meal Delivery Platform | [
"eess.SY",
"cs.AI",
"cs.SY"
] | To achieve high service quality and profitability, meal delivery platforms like Uber Eats and Grubhub must strategically operate their fleets to ensure timely deliveries for current orders while mitigating the consequential impacts of suboptimal decisions that leads to courier understaffing in the future. This study se... | {
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2501.05809 | AdaPRL: Adaptive Pairwise Regression Learning with Uncertainty
Estimation for Universal Regression Tasks | [
"cs.LG"
] | Current deep regression models usually learn in a point-wise way that treats each sample as an independent input, neglecting the relative ordering among different data. Consequently, the regression model could neglect the data's interrelationships, potentially resulting in suboptimal performance. Moreover, the existenc... | {
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2501.05813 | Social web and Wikipedia: an opportunity to rethink the links between
sources' credibility, trust and authority | [
"cs.IR",
"cs.CY",
"cs.SI"
] | The Web and its main tools (Google, Wikipedia, Facebook, Twitter) deeply raise and renew fundamental questions, that everyone asks almost every day: Is this information or content true? Can I trust this author or source? These questions are not new, they have been the same with books, newspapers, broadcasting and telev... | {
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2501.05815 | Enhanced sampled-data model predictive control via nonlinear lifting | [
"eess.SY",
"cs.SY"
] | This paper introduces a novel nonlinear model predictive control (NMPC) framework that incorporates a lifting technique to enhance control performance for nonlinear systems. While the lifting technique has been widely employed in linear systems to capture intersample behaviour, their application to nonlinear systems re... | {
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2501.05816 | IndoNLP 2025: Shared Task on Real-Time Reverse Transliteration for
Romanized Indo-Aryan languages | [
"cs.CL"
] | The paper overviews the shared task on Real-Time Reverse Transliteration for Romanized Indo-Aryan languages. It focuses on the reverse transliteration of low-resourced languages in the Indo-Aryan family to their native scripts. Typing Romanized Indo-Aryan languages using ad-hoc transliterals and achieving accurate nati... | {
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2501.05819 | Diffusion Models for Smarter UAVs: Decision-Making and Modeling | [
"cs.LG",
"cs.AI"
] | Unmanned Aerial Vehicles (UAVs) are increasingly adopted in modern communication networks. However, challenges in decision-making and digital modeling continue to impede their rapid advancement. Reinforcement Learning (RL) algorithms face limitations such as low sample efficiency and limited data versatility, further m... | {
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2501.05823 | PersonaHOI: Effortlessly Improving Personalized Face with Human-Object
Interaction Generation | [
"cs.CV"
] | We introduce PersonaHOI, a training- and tuning-free framework that fuses a general StableDiffusion model with a personalized face diffusion (PFD) model to generate identity-consistent human-object interaction (HOI) images. While existing PFD models have advanced significantly, they often overemphasize facial features ... | {
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2501.05826 | AI-Driven Diabetic Retinopathy Screening: Multicentric Validation of
AIDRSS in India | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Purpose: Diabetic retinopathy (DR) is a major cause of vision loss, particularly in India, where access to retina specialists is limited in rural areas. This study aims to evaluate the Artificial Intelligence-based Diabetic Retinopathy Screening System (AIDRSS) for DR detection and prevalence assessment, addressing the... | {
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2501.05828 | UltraRay: Full-Path Ray Tracing for Enhancing Realism in Ultrasound
Simulation | [
"cs.CV",
"cs.GR"
] | Traditional ultrasound simulators solve the wave equation to model pressure distribution fields, achieving high accuracy but requiring significant computational time and resources. To address this, ray tracing approaches have been introduced, modeling wave propagation as rays interacting with boundaries and scatterers.... | {
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2501.05835 | Fine-tuning is Not Fine: Mitigating Backdoor Attacks in GNNs with
Limited Clean Data | [
"cs.LG",
"cs.CR"
] | Graph Neural Networks (GNNs) have achieved remarkable performance through their message-passing mechanism. However, recent studies have highlighted the vulnerability of GNNs to backdoor attacks, which can lead the model to misclassify graphs with attached triggers as the target class. The effectiveness of recent promis... | {
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2501.05839 | Poetry in Pixels: Prompt Tuning for Poem Image Generation via Diffusion
Models | [
"cs.CV"
] | The task of text-to-image generation has encountered significant challenges when applied to literary works, especially poetry. Poems are a distinct form of literature, with meanings that frequently transcend beyond the literal words. To address this shortcoming, we propose a PoemToPixel framework designed to generate i... | {
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2501.05842 | Orthogonal projection-based regularization for efficient model
augmentation | [
"cs.LG",
"cs.SY",
"eess.SY"
] | Deep-learning-based nonlinear system identification has shown the ability to produce reliable and highly accurate models in practice. However, these black-box models lack physical interpretability, and often a considerable part of the learning effort is spent on capturing already expected/known behavior due to first-pr... | {
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2501.05844 | "Cause" is Mechanistic Narrative within Scientific Domains: An Ordinary
Language Philosophical Critique of "Causal Machine Learning" | [
"cs.LG"
] | Causal Learning has emerged as a major theme of research in statistics and machine learning in recent years, promising specific computational techniques to apply to datasets that reveal the true nature of cause and effect in a number of important domains. In this paper we consider the epistemology of recognizing true c... | {
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2501.05845 | Annealing Machine-assisted Learning of Graph Neural Network for
Combinatorial Optimization | [
"cs.AI",
"cs.LG"
] | While Annealing Machines (AM) have shown increasing capabilities in solving complex combinatorial problems, positioning themselves as a more immediate alternative to the expected advances of future fully quantum solutions, there are still scaling limitations. In parallel, Graph Neural Networks (GNN) have been recently ... | {
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2501.05848 | Isogeometric Analysis for 2D Magnetostatic Computations with Multi-level
B\'{e}zier Extraction for Local Refinement | [
"cs.CE"
] | Local refinement is vital for efficient numerical simulations. In the context of Isogeometric Analysis (IGA), hierarchical B-splines have gained prominence. The work applies the methodology of truncated hierarchical B-splines (THB-splines) as they keep additional properties. The framework is further enriched with B\'{e... | {
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2501.05851 | Identity-aware Feature Decoupling Learning for Clothing-change Person
Re-identification | [
"cs.CV"
] | Clothing-change person re-identification (CC Re-ID) has attracted increasing attention in recent years due to its application prospect. Most existing works struggle to adequately extract the ID-related information from the original RGB images. In this paper, we propose an Identity-aware Feature Decoupling (IFD) learnin... | {
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2501.05852 | MRI Patterns of the Hippocampus and Amygdala for Predicting Stages of
Alzheimer's Progression: A Minimal Feature Machine Learning Framework | [
"cs.CV",
"cs.LG"
] | Alzheimer's disease (AD) progresses through distinct stages, from early mild cognitive impairment (EMCI) to late mild cognitive impairment (LMCI) and eventually to AD. Accurate identification of these stages, especially distinguishing LMCI from EMCI, is crucial for developing pre-dementia treatments but remains challen... | {
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2501.05855 | ConSim: Measuring Concept-Based Explanations' Effectiveness with
Automated Simulatability | [
"cs.CL"
] | Concept-based explanations work by mapping complex model computations to human-understandable concepts. Evaluating such explanations is very difficult, as it includes not only the quality of the induced space of possible concepts but also how effectively the chosen concepts are communicated to users. Existing evaluatio... | {
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2501.05862 | Language-Inspired Relation Transfer for Few-shot Class-Incremental
Learning | [
"cs.CV"
] | Depicting novel classes with language descriptions by observing few-shot samples is inherent in human-learning systems. This lifelong learning capability helps to distinguish new knowledge from old ones through the increase of open-world learning, namely Few-Shot Class-Incremental Learning (FSCIL). Existing works to so... | {
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2501.05867 | Neural Network Verification is a Programming Language Challenge | [
"cs.PL",
"cs.LG",
"cs.LO"
] | Neural network verification is a new and rapidly developing field of research. So far, the main priority has been establishing efficient verification algorithms and tools, while proper support from the programming language perspective has been considered secondary or unimportant. Yet, there is mounting evidence that in... | {
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2501.05870 | A Neighbor-based Approach to Pitch Ownership Models in Soccer | [
"cs.LG"
] | Pitch ownership models allow many types of analysis in soccer and provide valuable assistance to tactical analysts in understanding the game's dynamics. The novelty they provide over event-based analysis is that tracking data incorporates context that event-based data does not possess, like player positioning. This pap... | {
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2501.05871 | Collaborative Content Moderation in the Fediverse | [
"cs.SI",
"cs.LG",
"cs.NI"
] | The Fediverse, a group of interconnected servers providing a variety of interoperable services (e.g. micro-blogging in Mastodon) has gained rapid popularity. This sudden growth, partly driven by Elon Musk's acquisition of Twitter, has created challenges for administrators though. This paper focuses on one particular ch... | {
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2501.05874 | VideoRAG: Retrieval-Augmented Generation over Video Corpus | [
"cs.CV",
"cs.AI",
"cs.CL",
"cs.IR",
"cs.LG"
] | Retrieval-Augmented Generation (RAG) is a powerful strategy to address the issue of generating factually incorrect outputs in foundation models by retrieving external knowledge relevant to queries and incorporating it into their generation process. However, existing RAG approaches have primarily focused on textual info... | {
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2501.05880 | TakuNet: an Energy-Efficient CNN for Real-Time Inference on Embedded UAV
systems in Emergency Response Scenarios | [
"cs.CV",
"cs.PF"
] | Designing efficient neural networks for embedded devices is a critical challenge, particularly in applications requiring real-time performance, such as aerial imaging with drones and UAVs for emergency responses. In this work, we introduce TakuNet, a novel light-weight architecture which employs techniques such as dept... | {
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2501.05882 | Solving nonograms using Neural Networks | [
"cs.AI",
"cs.NE"
] | Nonograms are logic puzzles in which cells in a grid must be colored or left blank according to the numbers that are located in its headers. In this study, we analyze different techniques to solve this type of logical problem using an Heuristic Algorithm, Genetic Algorithm, and Heuristic Algorithm with Neural Network. ... | {
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2501.05884 | Text-to-Edit: Controllable End-to-End Video Ad Creation via Multimodal
LLMs | [
"cs.CV"
] | The exponential growth of short-video content has ignited a surge in the necessity for efficient, automated solutions to video editing, with challenges arising from the need to understand videos and tailor the editing according to user requirements. Addressing this need, we propose an innovative end-to-end foundational... | {
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2501.05885 | EDNet: Edge-Optimized Small Target Detection in UAV Imagery -- Faster
Context Attention, Better Feature Fusion, and Hardware Acceleration | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Detecting small targets in drone imagery is challenging due to low resolution, complex backgrounds, and dynamic scenes. We propose EDNet, a novel edge-target detection framework built on an enhanced YOLOv10 architecture, optimized for real-time applications without post-processing. EDNet incorporates an XSmall detectio... | {
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2501.05891 | Affordably Fine-tuned LLMs Provide Better Answers to Course-specific
MCQs | [
"cs.CL",
"cs.AI"
] | In education, the capability of generating human-like text of Large Language Models (LLMs) inspired work on how they can increase the efficiency of learning and teaching. We study the affordability of these models for educators and students by investigating how LLMs answer multiple-choice questions (MCQs) with respect ... | {
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2501.05892 | Beyond Flat Text: Dual Self-inherited Guidance for Visual Text
Generation | [
"cs.CV"
] | In real-world images, slanted or curved texts, especially those on cans, banners, or badges, appear as frequently, if not more so, than flat texts due to artistic design or layout constraints. While high-quality visual text generation has become available with the advanced generative capabilities of diffusion models, t... | {
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2501.05894 | Text2Playlist: Generating Personalized Playlists from Text on Deezer | [
"cs.IR",
"cs.LG"
] | The streaming service Deezer heavily relies on the search to help users navigate through its extensive music catalog. Nonetheless, it is primarily designed to find specific items and does not lead directly to a smooth listening experience. We present Text2Playlist, a stand-alone tool that addresses these limitations. T... | {
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2501.05901 | Valley2: Exploring Multimodal Models with Scalable Vision-Language
Design | [
"cs.CV"
] | Recently, vision-language models have made remarkable progress, demonstrating outstanding capabilities in various tasks such as image captioning and video understanding. We introduce Valley2, a novel multimodal large language model designed to enhance performance across all domains and extend the boundaries of practica... | {
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2501.05903 | Discovery of sustainable energy materials via the machine-learned
material space | [
"cond-mat.mtrl-sci",
"cs.LG",
"physics.comp-ph"
] | Does a machine learning model actually gain an understanding of the material space? We answer this question in the affirmative on the example of the OptiMate model, a graph attention network trained to predict the optical properties of semiconductors and insulators. By applying the UMAP dimensionality reduction techniq... | {
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2501.05904 | Binary Event-Driven Spiking Transformer | [
"cs.CV"
] | Transformer-based Spiking Neural Networks (SNNs) introduce a novel event-driven self-attention paradigm that combines the high performance of Transformers with the energy efficiency of SNNs. However, the larger model size and increased computational demands of the Transformer structure limit their practicality in resou... | {
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2501.05906 | Q-MAML: Quantum Model-Agnostic Meta-Learning for Variational Quantum
Algorithms | [
"quant-ph",
"cs.LG"
] | In the Noisy Intermediate-Scale Quantum (NISQ) era, using variational quantum algorithms (VQAs) to solve optimization problems has become a key application. However, these algorithms face significant challenges, such as choosing an effective initial set of parameters and the limited quantum processing time that restric... | {
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2501.05921 | The New Anticipatory Governance Culture for Innovation: Regulatory
Foresight, Regulatory Experimentation and Regulatory Learning | [
"cs.CY",
"cs.AI"
] | With the rapid pace of technological innovation, traditional methods of policy formation and legislating are becoming conspicuously anachronistic. The need for regulatory choices to be made to counter the deadening effect of regulatory lag is more important to developing markets and fostering growth than achieving one ... | {
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2501.05925 | Navigating Tomorrow: Reliably Assessing Large Language Models
Performance on Future Event Prediction | [
"cs.CL",
"cs.IR"
] | Predicting future events is an important activity with applications across multiple fields and domains. For example, the capacity to foresee stock market trends, natural disasters, business developments, or political events can facilitate early preventive measures and uncover new opportunities. Multiple diverse computa... | {
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2501.05926 | LLMs Reproduce Stereotypes of Sexual and Gender Minorities | [
"cs.CL"
] | A large body of research has found substantial gender bias in NLP systems. Most of this research takes a binary, essentialist view of gender: limiting its variation to the categories _men_ and _women_, conflating gender with sex, and ignoring different sexual identities. But gender and sexuality exist on a spectrum, so... | {
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2501.05927 | Expressing One's Identity Online: Left-Right and cross EU-country
variation in self-representation in social media | [
"cs.SI"
] | We examine how social media users from eight European Union (EU) member states express their socio-political identities, focusing on users' online self-presentation and group identity cues conveyed through bios. Our goal is to explore commonalities and differences in topics discussed in social media profiles, across Le... | {
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2501.05928 | Towards Backdoor Stealthiness in Model Parameter Space | [
"cs.CR",
"cs.AI"
] | Recent research on backdoor stealthiness focuses mainly on indistinguishable triggers in input space and inseparable backdoor representations in feature space, aiming to circumvent backdoor defenses that examine these respective spaces. However, existing backdoor attacks are typically designed to resist a specific type... | {
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2501.05931 | Environment Modeling for Service Robots From a Task Execution
Perspective | [
"cs.RO"
] | Service robots are increasingly entering the home to provide domestic tasks for residents. However, when working in an open, dynamic, and unstructured home environment, service robots still face challenges such as low intelligence for task execution and poor long-term autonomy (LTA), which has limited their deployment.... | {
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2501.05932 | DiffuSETS: 12-lead ECG Generation Conditioned on Clinical Text Reports
and Patient-Specific Information | [
"cs.LG",
"cs.AI"
] | Heart disease remains a significant threat to human health. As a non-invasive diagnostic tool, the electrocardiogram (ECG) is one of the most widely used methods for cardiac screening. However, the scarcity of high-quality ECG data, driven by privacy concerns and limited medical resources, creates a pressing need for e... | {
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2501.05933 | Weakly Supervised Segmentation of Hyper-Reflective Foci with Compact
Convolutional Transformers and SAM2 | [
"cs.CV"
] | Weakly supervised segmentation has the potential to greatly reduce the annotation effort for training segmentation models for small structures such as hyper-reflective foci (HRF) in optical coherence tomography (OCT). However, most weakly supervised methods either involve a strong downsampling of input images, or only ... | {
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2501.05934 | Encoded Spatial Attribute in Multi-Tier Federated Learning | [
"cs.LG",
"cs.DC"
] | This research presents an Encoded Spatial Multi-Tier Federated Learning approach for a comprehensive evaluation of aggregated models for geospatial data. In the client tier, encoding spatial information is introduced to better predict the target outcome. The research aims to assess the performance of these models acros... | {
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2501.05936 | A Multimodal Dataset for Enhancing Industrial Task Monitoring and
Engagement Prediction | [
"cs.CV"
] | Detecting and interpreting operator actions, engagement, and object interactions in dynamic industrial workflows remains a significant challenge in human-robot collaboration research, especially within complex, real-world environments. Traditional unimodal methods often fall short of capturing the intricacies of these ... | {
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2501.05942 | Soft regression trees: a model variant and a decomposition training
algorithm | [
"cs.LG",
"math.OC"
] | Decision trees are widely used for classification and regression tasks in a variety of application fields due to their interpretability and good accuracy. During the past decade, growing attention has been devoted to globally optimized decision trees with deterministic or soft splitting rules at branch nodes, which are... | {
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2501.05943 | Koopman-Based Model Predictive Control of Functional Electrical
Stimulation for Ankle Dorsiflexion and Plantarflexion Assistance | [
"eess.SY",
"cs.SY"
] | Functional Electrical Stimulation (FES) can be an effective tool to augment paretic muscle function and restore normal ankle function. Our approach incorporates a real-time, data-driven Model Predictive Control (MPC) scheme, built upon a Koopman operator theory (KOT) framework. This framework adeptly captures the compl... | {
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2501.05945 | Reusable specimen-level inference in computational pathology | [
"eess.IV",
"cs.CV",
"q-bio.TO"
] | Foundation models for computational pathology have shown great promise for specimen-level tasks and are increasingly accessible to researchers. However, specimen-level models built on these foundation models remain largely unavailable, hindering their broader utility and impact. To address this gap, we developed SpinPa... | {
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2501.05946 | Coverage and Spectral Efficiency of NOMA-Enabled LEO Satellite Networks
with Ordering Schemes | [
"eess.SP",
"cs.IT",
"cs.SY",
"eess.SY",
"math.IT"
] | This paper investigates an analytical model for low-earth orbit (LEO) multi-satellite downlink non-orthogonal multiple access (NOMA) networks. The satellites transmit data to multiple NOMA user terminals (UTs), each employing successive interference cancellation (SIC) for decoding. Two ordering schemes are adopted for ... | {
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2501.05948 | Universal-2-TF: Robust All-Neural Text Formatting for ASR | [
"cs.CL"
] | This paper introduces an all-neural text formatting (TF) model designed for commercial automatic speech recognition (ASR) systems, encompassing punctuation restoration (PR), truecasing, and inverse text normalization (ITN). Unlike traditional rule-based or hybrid approaches, this method leverages a two-stage neural arc... | {
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2501.05952 | Scalable Vision Language Model Training via High Quality Data Curation | [
"cs.CV",
"cs.CL"
] | In this paper, we introduce SAIL-VL (ScAlable Vision Language Model TraIning via High QuaLity Data Curation), an open-source vision language model (VLM) series achieving state-of-the-art (SOTA) performance in 2B and 8B parameters. The following three key improvements contribute to SAIL-VL's leading performance: (1) Sca... | {
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2501.05961 | Swin-X2S: Reconstructing 3D Shape from 2D Biplanar X-ray with Swin
Transformers | [
"cs.CV",
"eess.IV"
] | The conversion from 2D X-ray to 3D shape holds significant potential for improving diagnostic efficiency and safety. However, existing reconstruction methods often rely on hand-crafted features, manual intervention, and prior knowledge, resulting in unstable shape errors and additional processing costs. In this paper, ... | {
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2501.05962 | Effective faking of verbal deception detection with target-aligned
adversarial attacks | [
"cs.CL",
"cs.AI"
] | Background: Deception detection through analysing language is a promising avenue using both human judgments and automated machine learning judgments. For both forms of credibility assessment, automated adversarial attacks that rewrite deceptive statements to appear truthful pose a serious threat. Methods: We used a dat... | {
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2501.05963 | Finnish SQuAD: A Simple Approach to Machine Translation of Span
Annotations | [
"cs.CL"
] | We apply a simple method to machine translate datasets with span-level annotation using the DeepL MT service and its ability to translate formatted documents. Using this method, we produce a Finnish version of the SQuAD2.0 question answering dataset and train QA retriever models on this new dataset. We evaluate the qua... | {
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2501.05964 | Recommender Systems for Social Good: The Role of Accountability and
Sustainability | [
"cs.IR"
] | This work examines the role of recommender systems in promoting sustainability, social responsibility, and accountability, with a focus on alignment with the United Nations Sustainable Development Goals (SDGs). As recommender systems become increasingly integrated into daily interactions, they must go beyond personaliz... | {
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2501.05965 | Model Inversion in Split Learning for Personalized LLMs: New Insights
from Information Bottleneck Theory | [
"cs.LG"
] | Personalized Large Language Models (LLMs) have become increasingly prevalent, showcasing the impressive capabilities of models like GPT-4. This trend has also catalyzed extensive research on deploying LLMs on mobile devices. Feasible approaches for such edge-cloud deployment include using split learning. However, previ... | {
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2501.05966 | Towards Early Prediction of Self-Supervised Speech Model Performance | [
"cs.SD",
"cs.CL",
"cs.LG",
"eess.AS"
] | In Self-Supervised Learning (SSL), pre-training and evaluation are resource intensive. In the speech domain, current indicators of the quality of SSL models during pre-training, such as the loss, do not correlate well with downstream performance. Consequently, it is often difficult to gauge the final downstream perform... | {
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2501.05970 | A Brain Age Residual Biomarker (BARB): Leveraging MRI-Based Models to
Detect Latent Health Conditions in U.S. Veterans | [
"cs.LG"
] | Age prediction using brain imaging, such as MRIs, has achieved promising results, with several studies identifying the model's residual as a potential biomarker for chronic disease states. In this study, we developed a brain age predictive model using a dataset of 1,220 U.S. veterans (18--80 years) and convolutional ne... | {
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2501.05981 | Hermit Kingdom Through the Lens of Multiple Perspectives: A Case Study
of LLM Hallucination on North Korea | [
"cs.CL"
] | Hallucination in large language models (LLMs) remains a significant challenge for their safe deployment, particularly due to its potential to spread misinformation. Most existing solutions address this challenge by focusing on aligning the models with credible sources or by improving how models communicate their confid... | {
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} |
2501.05982 | Deep Variational Sequential Monte Carlo for High-Dimensional
Observations | [
"cs.LG",
"eess.SP"
] | Sequential Monte Carlo (SMC), or particle filtering, is widely used in nonlinear state-space systems, but its performance often suffers from poorly approximated proposal and state-transition distributions. This work introduces a differentiable particle filter that leverages the unsupervised variational SMC objective to... | {
"Other": 0,
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} |
2501.05984 | The Safe Trusted Autonomy for Responsible Space Program | [
"eess.SY",
"cs.SY"
] | The Safe Trusted Autonomy for Responsible Space (STARS) program aims to advance autonomy technologies for space by leveraging machine learning technologies while mitigating barriers to trust, such as uncertainty, opaqueness, brittleness, and inflexibility. This paper presents the achievements and lessons learned from t... | {
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} |
2501.05987 | Comparing Self-Supervised Learning Models Pre-Trained on Human Speech
and Animal Vocalizations for Bioacoustics Processing | [
"cs.LG",
"eess.AS"
] | Self-supervised learning (SSL) foundation models have emerged as powerful, domain-agnostic, general-purpose feature extractors applicable to a wide range of tasks. Such models pre-trained on human speech have demonstrated high transferability for bioacoustic processing. This paper investigates (i) whether SSL models pr... | {
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} |
2501.05989 | Addressing speaker gender bias in large scale speech translation systems | [
"cs.CL",
"cs.AI"
] | This study addresses the issue of speaker gender bias in Speech Translation (ST) systems, which can lead to offensive and inaccurate translations. The masculine bias often found in large-scale ST systems is typically perpetuated through training data derived from Machine Translation (MT) systems. Our approach involves ... | {
"Other": 0,
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} |
2501.05990 | Constraining constructions with WordNet: pros and cons for the semantic
annotation of fillers in the Italian Constructicon | [
"cs.CL"
] | The paper discusses the role of WordNet-based semantic classification in the formalization of constructions, and more specifically in the semantic annotation of schematic fillers, in the Italian Constructicon. We outline how the Italian Constructicon project uses Open Multilingual WordNet topics to represent semantic f... | {
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} |
2501.05991 | An Attention-Guided Deep Learning Approach for Classifying 39 Skin
Lesion Types | [
"eess.IV",
"cs.CV",
"cs.LG"
] | The skin, as the largest organ of the human body, is vulnerable to a diverse array of conditions collectively known as skin lesions, which encompass various dermatoses. Diagnosing these lesions presents significant challenges for medical practitioners due to the subtle visual differences that are often imperceptible to... | {
"Other": 0,
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} |
2501.05994 | On the Interaction in Transient Stability of Two-Inverter Power Systems
containing GFL inverter Using Manifold Method | [
"eess.SY",
"cs.SY"
] | Many renewable energy resources are integrated into power systems via grid-following (GFL) inverters which rely on a phase-locked loop (PLL) for grid synchronization. During severe grid faults, GFL inverters are vulnerable to transient instability, often leading to disconnection from the grid. This paper aims to elucid... | {
"Other": 0,
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"cs.SY": 1
} |
2501.05997 | Minimizing Occlusion Effect on Multi-View Camera Perception in BEV with
Multi-Sensor Fusion | [
"cs.CV"
] | Autonomous driving technology is rapidly evolving, offering the potential for safer and more efficient transportation. However, the performance of these systems can be significantly compromised by the occlusion on sensors due to environmental factors like dirt, dust, rain, and fog. These occlusions severely affect visi... | {
"Other": 0,
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} |
2501.06000 | Self-Supervised Partial Cycle-Consistency for Multi-View Matching | [
"cs.CV"
] | Matching objects across partially overlapping camera views is crucial in multi-camera systems and requires a view-invariant feature extraction network. Training such a network with cycle-consistency circumvents the need for labor-intensive labeling. In this paper, we extend the mathematical formulation of cycle-consist... | {
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} |
2501.06002 | DeltaGNN: Graph Neural Network with Information Flow Control | [
"cs.LG"
] | Graph Neural Networks (GNNs) are popular deep learning models designed to process graph-structured data through recursive neighborhood aggregations in the message passing process. When applied to semi-supervised node classification, the message-passing enables GNNs to understand short-range spatial interactions, but al... | {
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} |
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